from langchain.llms.base import LLM
from typing import Optional, List
from langchain.llms.utils import enforce_stop_tokens
from transformers import AutoTokenizer, AutoModel

"""ChatGLM_G is a wrapper around the ChatGLM model to fit LangChain framework. May not be an optimal implementation"""

tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()

class ChatGLM(LLM):
    max_token: int = 10000
    temperature: float = 0.1
    top_p = 0.9
    history = []

    def __init__(self):
        super().__init__()

    @property
    def _llm_type(self) -> str:
        return "ChatGLM"

    def _call(self,
              prompt: str,
              stop: Optional[List[str]] = None) -> str:
        response, updated_history = model.chat(
            tokenizer,
            prompt,
            history=self.history,
            max_length=self.max_token,
            temperature=self.temperature,

        )
        print("history: ", self.history)
        if stop is not None:
            response = enforce_stop_tokens(response, stop)
        self.history = updated_history
        return response